From 3D Model Data to Semantics

Abstract

The semantic-based 3D models retrieval systems have become necessary since the increase of 3D models databases. In this paper, we propose a new method for the mapping problem between 3D model data and semantic data involved in semantic based retrieval for 3D models given by polygonal meshes. First, we focused on extracting invariant descriptors from the 3D models and analyzing them to efficient semantic annotation and to improve the retrieval accuracy. Selected shape descriptors provide a set of terms commonly used to describe visually a set of objects using linguistic terms and are used as semantic concept to label 3D model. Second, spatial relationship representing directional, topological and distance relationships are used to derive other high-level semantic features and to avoid the problem of automatic 3D model annotation. Based on the resulting semantic annotation and spatial concepts, an ontology for 3D model retrieval is constructed and other concepts can be inferred. This ontology is used to find similar 3D models for a given query model. We adopted the query by semantic example approach, in which the annotation is performed mostly automatically. The proposed method is implemented in our 3D search engine (SB3DMR), tested using the Princeton Shape Benchmark Database.

The impact of surface structure and band gap on the optoelectronic properties of Cu2O nanoclusters of varying size and symmetry

“…Whereas, surface area to volume ratio (S/V) is termed as shape dissimilarity descriptor and measures the dissimilarity between two 3D shapes. [95][96][97][98][99] Though, the dependence of absorption and scattering properties of the nano particle on the S/V ratio is well-cultivated; 2,28,90-103 the same dependence on the symmetry needs a detail attention. According to nanothermodynamics, 104 with increase of surface area the surface energy increases due to increment of density of elastic strain energy of lattice relaxation, density of dangling bonds and coordination imperfection.…”

BERT Representation for Arabic Information Retrieval: A Comparative Study

“…The number of ontology-based semantic search engines had been continuously growing in the last decade. In our previous work [22], we used an ontology to build a semantic search engine and showed improved results for search and retrieval in a 3D database. However, the semantics supported by ontology wasnot sufficient and does not help much in semantic information retrieval, especially for searching text.…”

Smart Point Cloud: Definition and Remaining Challenges

“…Similarity-based solutions apply to point cloud segmentation techniques by searching a database for similar models while assessing segmentation on a context-dependent matter but the objects within the point cloud should be well defined with precise attributes. (Kassimi and Beqqali, 2011) use shape indexes to induce a semantic ontology based model, that is included in a learning process to directly label the 3D model, increasing automation. However, while proposing a way to infer knowledge in segmentation and classification method, these papers rarely cover the topic of data structuration.…”